descriptive_statistics
2024-12-03
Descriptive Statistics of NYC Shooting Data
library(ggplot2)
library(readxl)
library(readr)
library(tidyverse)
library(dplyr)
library(sf)
library(plotly)
library(geojsonio)
library(knitr)
library(tidyr)# Load the dataset
df_descriptive=read_csv("data_final.csv")
data_final <- read_csv("data_final.csv")Plots that are related to Time
Shooting Incidents by Year in NYC
# Grouping the data by year to count the number of incidents
df_descriptive$Year <- as.factor(df_descriptive$Year)
year_counts <- as.data.frame(table(df_descriptive$Year))
colnames(year_counts) <- c('Year', 'Count')
# Plotting the line chart for shooting incidents by year with data points labeled
ggplot(year_counts, aes(x = Year, y = Count, group = 1)) +
geom_line(color = 'blue') +
geom_point(size = 3) +
geom_text(aes(label = Count), vjust = -0.5, size = 3) +
labs(title = 'Shooting Incidents by Year in NYC',
x = 'Year',
y = 'Number of Shooting Incidents') +
theme_minimal()
The chart shows a stable trend in shooting incidents from 2017 to 2019, followed by a sharp increase in 2020, likely linked to socio-economic factors like the COVID-19 pandemic. Incidents peaked in 2021 and then declined through 2023.
Bar Chart of Shooting Incidents by Month in NYC
# Grouping the data by month
df_descriptive$Month <- as.factor(df_descriptive$Month)
month_counts <- as.data.frame(table(df_descriptive$Month))
colnames(month_counts) <- c('Month', 'Count')
# Plotting the bar chart for shooting incidents by month
ggplot(month_counts, aes(x = Month, y = Count)) +
geom_bar(stat = 'identity', fill = 'skyblue') +
geom_text(aes(label = Count), vjust = -0.5) +
labs(title = 'Bar Chart of Shooting Incidents by Month in NYC',
x = 'Month',
y = 'Number of Shooting Incidents') +
theme_minimal()
The bar chart shows a seasonal trend in shooting incidents in NYC, with a significant increase during the warmer months, peaking in July (1248 incidents). The highest numbers are seen in June, July, and August, while the lowest are in February (494 incidents) and January (617 incidents), suggesting fewer incidents during colder months. The gradual rise from January to July, followed by a decline towards December, indicates a correlation between warmer weather and increased incidents, potentially due to increased outdoor activities.
Density Plot of Shooting Incident Occurrence Time in NYC
# Convert OCCUR_TIME to a proper time format
df_descriptive$OCCUR_TIME <- format(strptime(df_descriptive$OCCUR_TIME, format = "%H:%M:%S"), "%H:%M:%S")
# Plotting the density of the occurrence time
ggplot(df_descriptive, aes(x = as.POSIXct(OCCUR_TIME, format = "%H:%M:%S"))) +
geom_density(fill = 'skyblue', alpha = 0.6) +
labs(title = 'Density Plot of Shooting Incident Occurrence Time in NYC',
x = 'Time of Occurrence',
y = 'Density') +
scale_x_datetime(date_labels = "%H:%M") +
theme_minimal()
The density plot shows changes in shooting incident trends throughout the day in NYC, with two distinct peaks. The first peak occurs in the late night to early morning hours, and the second peak appears in the evening. The lowest density is observed in the late morning to early afternoon.
# Bar chart showing the number of TRUE and FALSE in the Sky_Is_Dark variable
sky_dark_counts <- df_descriptive %>%
group_by(Sky_Is_Dark) %>%
summarise(total_incidents = n())
ggplot(sky_dark_counts, aes(x = Sky_Is_Dark, y = total_incidents, fill = Sky_Is_Dark)) +
geom_bar(stat = 'identity') +
scale_x_discrete(labels = c("FALSE" = "Daytime", "TRUE" = "Nighttime")) + # Update x-axis labels
scale_fill_manual(values = c("FALSE" = "skyblue", "TRUE" = "darkblue"),
labels = c("FALSE" = "Daytime", "TRUE" = "Nighttime")) +
labs(title = 'Number of Shooting Incidents by the time of a day (Daytime vs. Nigttime)',
x = 'Time of a day(Daytime vs. Nigttime)',
y = 'Number of Shooting Incidents') +
theme_minimal()
The number of shooting incidents when the sky was dark is significantly higher than when it was bright. This might indicate that shootings are more likely to occur during nighttime or low visibility conditions. The higher number of incidents during dark conditions could be due to factors such as reduced visibility, higher activity at night, or fewer people around, making it easier for incidents to occur undetected.
EDA sorted by neighborhood
Table showing Top 10 total incident NTA in Each Borough
# Table showing Top 10 total incident NTA in Each Borough
incident_by_nta_borough <- df_descriptive %>%
drop_na() %>%
group_by(BORO, NTA) %>%
summarise(total_incidents = n()) %>%
arrange(BORO, desc(total_incidents)) %>%
group_by(BORO) %>%
slice_max(n = 10, order_by = total_incidents) %>%
select(-total_incidents) %>%
group_by(BORO) %>%
mutate(row_num = row_number()) %>%
pivot_wider(names_from = BORO, values_from = NTA) %>%
unnest(cols = c(BRONX, BROOKLYN, MANHATTAN, QUEENS, `STATEN ISLAND`)) %>%
select(row_num, everything())%>%
slice(1:10)
# Display the table in the desired format
kable(incident_by_nta_borough, caption = "Top 10 NTAs with Total Shooting Incidents in Each Borough (2017-2023)")| row_num | BRONX | BROOKLYN | MANHATTAN | QUEENS | STATEN ISLAND |
|---|---|---|---|---|---|
| 1 | Mott Haven-Port Morris | Brownsville | East Harlem (North) | Far Rockaway-Bayswater | St. George-New Brighton |
| 2 | Mount Hope | Bedford-Stuyvesant (East) | Harlem (North) | Baisley Park | Tompkinsville-Stapleton-Clifton-Fox Hills |
| 3 | Tremont | Crown Heights (North) | East Harlem (South) | Richmond Hill | West New Brighton-Silver Lake-Grymes Hill |
| 4 | Mount Eden-Claremont (West) | East New York-New Lots | Harlem (South) | Jamaica | Mariner’s Harbor-Arlington-Graniteville |
| 5 | Concourse-Concourse Village | East Flatbush-Remsen Village | Washington Heights (South) | Rockaway Beach-Arverne-Edgemere | Annadale-Huguenot-Prince’s Bay-Woodrow |
| 6 | Williamsbridge-Olinville | East New York (North) | Chelsea-Hudson Yards | St. Albans | Port Richmond |
| 7 | Melrose | Bedford-Stuyvesant (West) | Hamilton Heights-Sugar Hill | South Ozone Park | Rosebank-Shore Acres-Park Hill |
| 8 | Belmont | Canarsie | Inwood | Elmhurst | Grasmere-Arrochar-South Beach-Dongan Hills |
| 9 | Longwood | Coney Island-Sea Gate | Chinatown-Two Bridges | South Jamaica | Great Kills-Eltingville |
| 10 | Fordham Heights | East New York-City Line | Washington Heights (North) | Astoria (East)-Woodside (North) | New Dorp-Midland Beach |
Table showing Top 10 NTAs with the Highest Incident Rate
# Get the Top 10 NTAs with the Highest Incident Rate by Year
top_10_incident_rate <- df_descriptive %>%
distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
filter(NTAType == 'Residential') %>%
arrange(desc(incident_rate_by_year_nta)) %>%
slice(1:10)
# Pull information for the Top 10 NTAs
top_10_nta_incident_rate <- top_10_incident_rate %>%
select(NTA, BORO, incident_rate = incident_rate_by_year_nta,
total_population = Total_population_nta,
percent_poverty = Percent_poverty,
percent_education = Percent_education)
# Display the table in the desired format
kable(top_10_nta_incident_rate, digits = 6, caption = "Top 10 NTAs with the Highest Incident Rate by Year in Each Borough")| NTA | BORO | incident_rate | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| Claremont Village-Claremont (East) | BRONX | 0.187350 | 24553 | 43.2 | 61.7 |
| Brownsville | BROOKLYN | 0.153795 | 60470 | 37.2 | 76.9 |
| Tremont | BRONX | 0.118196 | 32150 | 36.3 | 71.4 |
| Inwood | MANHATTAN | 0.115706 | 36299 | 15.3 | 77.0 |
| East New York-New Lots | BROOKLYN | 0.107539 | 53004 | 25.3 | 82.6 |
| Hunts Point | BRONX | 0.105743 | 15131 | 34.3 | 72.2 |
| Mount Eden-Claremont (West) | BRONX | 0.100703 | 49651 | 31.8 | 67.9 |
| Brownsville | BROOKLYN | 0.099223 | 60470 | 37.2 | 76.9 |
| Brownsville | BROOKLYN | 0.097569 | 60470 | 37.2 | 76.9 |
| Tremont | BRONX | 0.096423 | 32150 | 36.3 | 71.4 |
EDA related to Victims
Plot of Description of Male and Female Shooting Victims by Age Group and Race
# Plot: Description of Victims
victim_summary <- df_descriptive %>%
filter(VIC_SEX %in% c("M", "F")) %>% # Filter out unknown
group_by(VIC_SEX, VIC_AGE_GROUP, VIC_RACE) %>%
summarise(total_victims = n()) %>%
drop_na()
# Plotting the description of male victims
ggplot(victim_summary %>% filter(VIC_SEX == "M"), aes(x = VIC_AGE_GROUP, y = total_victims, fill = VIC_RACE)) +
geom_bar(stat = 'identity', position = 'dodge') +
labs(title = "Description of Male Shooting Victims by Age Group and Race",
x = "Victim Age Group",
y = "Number of Victims",
fill = "Victim Race") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Plotting the description of female victims
ggplot(victim_summary %>% filter(VIC_SEX == "F"), aes(x = VIC_AGE_GROUP, y = total_victims, fill = VIC_RACE)) +
geom_bar(stat = 'identity', position = 'dodge') +
labs(title = "Description of Female Shooting Victims by Age Group and Race",
x = "Victim Age Group",
y = "Number of Victims",
fill = "Victim Race") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
The bar charts show the distribution of male and female shooting victims by age group and race in NYC. In both charts, Black victims are the most affected across all age groups, with the highest number of victims in the 18-24 and 25-44 age groups. For males, the 25-44 age group shows a notable peak, while for females, the same age group also has the highest numbers. White Hispanic victims also show considerable numbers, particularly in the 25-44 age group.
Plot: Bar chart showing all victim’s age
# Plot: Bar chart showing victim's age
ggplot(df_descriptive %>% filter(!is.na(VIC_AGE_GROUP)& VIC_AGE_GROUP != 1022), aes(x = VIC_AGE_GROUP)) +
geom_bar(fill = "steelblue") +
labs(title = "Bar Chart of Victim's Age",
x = "Victim Age Group",
y = "Count of Victims") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
The 25-44 age group has the highest number of victims, significantly more than any other group. The 18-24 age group also shows a considerable number of victims, while the <18 and 45-64 age groups have notably fewer victims. The 65+ age group has the least number of victims.
Table showing Top 10 NTAs with the Highest Total Shooting Incidents
# Table showing Top 10 NTAs with the Highest Total Shooting Incidents
top_10_nta_overall <- df_descriptive %>%
group_by(BORO, NTA) %>%
summarise(total_incidents = n(),
total_population = first(Total_population_nta),
percent_poverty = first(Percent_poverty),
percent_education = first(Percent_education)) %>%
arrange(desc(total_incidents)) %>%
ungroup() %>%
slice(1:10) %>%
select(BORO, NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents")| BORO | NTA | total_incidents | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| BROOKLYN | Brownsville | 350 | 60470 | 37.2 | 76.9 |
| BROOKLYN | Bedford-Stuyvesant (East) | 323 | 84653 | 22.2 | 87.8 |
| MANHATTAN | Harlem (North) | 253 | 83327 | 25.1 | 82.9 |
| BROOKLYN | Crown Heights (North) | 235 | 85275 | 20.5 | 84.1 |
| MANHATTAN | East Harlem (North) | 229 | 64655 | 32.3 | 75.9 |
| BRONX | Mott Haven-Port Morris | 207 | 57718 | 43.7 | 62.0 |
| BROOKLYN | Bedford-Stuyvesant (West) | 207 | 89189 | 26.0 | 84.8 |
| BROOKLYN | East New York-New Lots | 205 | 53004 | 25.3 | 82.6 |
| BRONX | Concourse-Concourse Village | 169 | 69387 | 31.9 | 70.5 |
| BRONX | Mount Eden-Claremont (West) | 166 | 49651 | 31.8 | 67.9 |
Plot Total Number of Incidents Across NYC BOROs (2017-2023)
Process of data manipulation
Plot of Total Number of Incidents Across NYC BOROs (2017-2023)
data_final <- read_csv("data_final.csv")
boro_map_data <- boro_map_data %>%
mutate(
hover_text = paste("Borough:", boro_name, "<br>Total Incidents:", Number_of_Incidents)
)
# Create the interactive plot with click functionality
plot <- plot_ly(
data = boro_map_data,
type = "scattermapbox",
split = ~boro_name, # Separate polygons by boroughs
color = ~Number_of_Incidents, # Color based on the number of incidents
colors = "viridis", # Use a color scale
text = ~hover_text, # Display hover text
hoverinfo = "text",
marker = list(size = 8, opacity = 0.7)
) %>%
layout(
title = "Total Number of Incidents Across NYC BOROs (2017-2023)",
mapbox = list(
style = "carto-positron", # Base map style
center = list(lon = -74.0060, lat = 40.7128), # Center map on NYC
zoom = 10
)
)
# Add click functionality to display the borough name and number of incidents
plot <- plot %>%
event_register("plotly_click") %>%
htmlwidgets::onRender("
function(el, x) {
el.on('plotly_click', function(d) {
var point = d.points[0];
var text = point.text;
alert('You clicked on: ' + text);
});
}
")
# Display the interactive plot
plotThe map shows the total number of incidents across NYC boroughs (2017–2023), with each borough represented by a distinct color. A gradient is used to indicate the magnitude of incidents, with darker shades corresponding to higher counts, ranging from 0 to over 3,000 incidents. Each borough is outlined and filled with its respective color, making it easy to distinguish. The legend on the right identifies the boroughs (Bronx, Brooklyn, Manhattan, Queens, Staten Island) and aligns with the color gradient to show the number of incidents. This visualization highlights geographical disparities in incident frequency across the boroughs, aiding in understanding spatial distribution patterns.
Plot Total Number of Incidents Across NYC CDTAs from 2017 to 2023
Removing NA
## There is space between letter and number in CDTA, I deleted the space below
data_final$CDTA <- gsub(" ", "", data_final$CDTA)
cdta_incident_counts <- data_final %>%
group_by(CDTA) %>%
summarise(Number_of_Incidents = n(), .groups = "drop")
# Remove any trailing spaces or mismatches in CDTA identifiers:
cdta_shape$CDTA2020 <- gsub(" ", "", cdta_shape$CDTA2020)
data_final$CDTA <- gsub(" ", "", data_final$CDTA)
# Identify Missing Matches
unmatched_cdta <- setdiff(cdta_shape$CDTA2020, data_final$CDTA)
#Re-Merge the Data
cdta_map_data <- cdta_shape %>%
left_join(cdta_incident_counts, by = c("CDTA2020" = "CDTA"))
# Update NA Handling
cdta_map_data <- cdta_map_data %>%
mutate(
Number_of_Incidents = ifelse(is.na(Number_of_Incidents), 0, Number_of_Incidents),
Incident_Range = cut(
Number_of_Incidents,
breaks = seq(0, 600, by = 120),
labels = c("0-120", "121-240", "241-360", "361-480", "481-600"),
include.lowest = TRUE
)
)“QN80” “SI95” “QN84” are my NAs. There is no shooting incident in these CDTAs. I assigned these 3 CDTA with 0 incident.
Plot of Total Number of Incidents Across NYC CDTAs from 2017 to 2023
ggplot(data = cdta_map_data) +
geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") + # Add labels+
scale_fill_manual(
values = c(
"0-120" = "#b2e2e2",
"121-240" = "skyblue",
"241-360" = "#66c2a4",
"361-480" = "#2ca25f",
"481-600" = "#006d2c"
),
name = "Number of Incidents"
) +
labs(
title = "Total Number of Incidents Across NYC CDTAs from 2017 to 2023",
subtitle = "Incidents grouped by range (0-600, 120 breaks)",
caption = "Data Source: Your dataset"
) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
The map shows NYC CDTA incidents (2017–2023) using a gradient from light blue (fewer incidents) to dark green (more incidents), with counts labeled on each district. Dark green areas highlight hotspots, likely in densely populated regions, while lighter blue areas, like Staten Island, show fewer incidents. This visualization helps identify trends and prioritize safety efforts.
for shiny
Top 10 NTAs with the Highest Total Shooting Incidents in each boro
MANHATTAN
# Table showing Top 10 NTAs with the Highest Total Shooting Incidents in MANHATTAN
top_10_nta_overall_manhattan <- df_descriptive %>%
filter(BORO == 'MANHATTAN') %>%
group_by(BORO, NTA) %>%
summarise(total_incidents = n(),
total_population = first(Total_population_nta),
percent_poverty = first(Percent_poverty),
percent_education = first(Percent_education)) %>%
arrange(desc(total_incidents)) %>%
ungroup() %>%
slice(1:10) %>%
select(BORO, NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_manhattan, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in MANHATTAN")| BORO | NTA | total_incidents | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| MANHATTAN | Harlem (North) | 253 | 83327 | 25.1 | 82.9 |
| MANHATTAN | East Harlem (North) | 229 | 64655 | 32.3 | 75.9 |
| MANHATTAN | East Harlem (South) | 145 | 59814 | 29.0 | 78.3 |
| MANHATTAN | Harlem (South) | 130 | 47113 | 18.8 | 87.4 |
| MANHATTAN | Inwood | 105 | 36299 | 15.3 | 77.0 |
| MANHATTAN | Washington Heights (South) | 88 | 72037 | 19.9 | 70.3 |
| MANHATTAN | Washington Heights (North) | 67 | 71842 | 14.8 | 79.3 |
| MANHATTAN | Hamilton Heights-Sugar Hill | 54 | 49410 | 24.0 | 78.6 |
| MANHATTAN | Manhattanville-West Harlem | 46 | 22183 | 26.7 | 77.4 |
| MANHATTAN | East Village | 41 | 71436 | 24.3 | 87.7 |
BROOKLYN
# Table showing Top 10 NTAs with the Highest Total Shooting Incidents in BROOKLYN
top_10_nta_overall_brooklyn <- df_descriptive %>%
filter(BORO == 'BROOKLYN') %>%
group_by(BORO, NTA) %>%
summarise(total_incidents = n(),
total_population = first(Total_population_nta),
percent_poverty = first(Percent_poverty),
percent_education = first(Percent_education)) %>%
arrange(desc(total_incidents)) %>%
ungroup() %>%
slice(1:10) %>%
select(BORO, NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_brooklyn, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in BROOKLYN")| BORO | NTA | total_incidents | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| BROOKLYN | Brownsville | 350 | 60470 | 37.2 | 76.9 |
| BROOKLYN | Bedford-Stuyvesant (East) | 323 | 84653 | 22.2 | 87.8 |
| BROOKLYN | Crown Heights (North) | 235 | 85275 | 20.5 | 84.1 |
| BROOKLYN | Bedford-Stuyvesant (West) | 207 | 89189 | 26.0 | 84.8 |
| BROOKLYN | East New York-New Lots | 205 | 53004 | 25.3 | 82.6 |
| BROOKLYN | Canarsie | 166 | 89932 | 13.0 | 88.1 |
| BROOKLYN | East New York (North) | 148 | 42818 | 25.5 | 79.4 |
| BROOKLYN | East Flatbush-Remsen Village | 126 | 39115 | 18.7 | 83.7 |
| BROOKLYN | Ocean Hill | 120 | 37952 | 27.3 | 83.1 |
| BROOKLYN | Flatbush | 111 | 66503 | 13.7 | 87.8 |
BRONX
# Table showing Top 10 NTAs with the Highest Total Shooting Incidents in BROOKLYN
top_10_nta_overall_bronx <- df_descriptive %>%
filter(BORO == 'BRONX') %>%
group_by(BORO, NTA) %>%
summarise(total_incidents = n(),
total_population = first(Total_population_nta),
percent_poverty = first(Percent_poverty),
percent_education = first(Percent_education)) %>%
arrange(desc(total_incidents)) %>%
ungroup() %>%
slice(1:10) %>%
select(BORO, NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_bronx, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in BRONX")| BORO | NTA | total_incidents | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| BRONX | Mott Haven-Port Morris | 207 | 57718 | 43.7 | 62.0 |
| BRONX | Concourse-Concourse Village | 169 | 69387 | 31.9 | 70.5 |
| BRONX | Mount Eden-Claremont (West) | 166 | 49651 | 31.8 | 67.9 |
| BRONX | Williamsbridge-Olinville | 158 | 61346 | 21.0 | 76.6 |
| BRONX | Melrose | 152 | 42651 | 40.7 | 68.2 |
| BRONX | Mount Hope | 149 | 49099 | 30.2 | 66.8 |
| BRONX | Tremont | 138 | 32150 | 36.3 | 71.4 |
| BRONX | Longwood | 130 | 40289 | 32.0 | 66.9 |
| BRONX | Belmont | 116 | 35825 | 39.6 | 65.6 |
| BRONX | Fordham Heights | 112 | 32099 | 33.2 | 66.0 |
QUEENS
# Table showing Top 10 NTAs with the Highest Total Shooting Incidents in BROOKLYN
top_10_nta_overall_queens <- df_descriptive %>%
filter(BORO == 'QUEENS') %>%
group_by(BORO, NTA) %>%
summarise(total_incidents = n(),
total_population = first(Total_population_nta),
percent_poverty = first(Percent_poverty),
percent_education = first(Percent_education)) %>%
arrange(desc(total_incidents)) %>%
ungroup() %>%
slice(1:10) %>%
select(BORO, NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_queens, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in QUEENS")| BORO | NTA | total_incidents | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| QUEENS | Far Rockaway-Bayswater | 116 | 58648 | 18.3 | 79.9 |
| QUEENS | Rockaway Beach-Arverne-Edgemere | 100 | 41367 | 22.1 | 82.1 |
| QUEENS | St. Albans | 93 | 51816 | 8.6 | 88.8 |
| QUEENS | South Jamaica | 91 | 44401 | 13.4 | 78.9 |
| QUEENS | Baisley Park | 83 | 43090 | 11.4 | 83.5 |
| QUEENS | Jamaica | 82 | 60993 | 15.7 | 70.4 |
| QUEENS | Queensbridge-Ravenswood-Dutch Kills | 79 | 32954 | 16.5 | 85.0 |
| QUEENS | South Ozone Park | 66 | 79540 | 9.7 | 76.7 |
| QUEENS | Richmond Hill | 55 | 34100 | 10.6 | 79.8 |
| QUEENS | Laurelton | 45 | 26088 | 5.4 | 91.2 |
STATEN ISLAND
# Table showing Top 10 NTAs with the Highest Total Shooting Incidents in BROOKLYN
top_10_nta_overall_staten <- df_descriptive %>%
filter(BORO == 'STATEN ISLAND') %>%
group_by(BORO, NTA) %>%
summarise(total_incidents = n(),
total_population = first(Total_population_nta),
percent_poverty = first(Percent_poverty),
percent_education = first(Percent_education)) %>%
arrange(desc(total_incidents)) %>%
ungroup() %>%
slice(1:10) %>%
select(BORO, NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_staten, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in STATEN ISLAND")| BORO | NTA | total_incidents | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| STATEN ISLAND | St. George-New Brighton | 48 | 20549 | 18.5 | 79.9 |
| STATEN ISLAND | Mariner’s Harbor-Arlington-Graniteville | 46 | 33492 | 16.8 | 83.3 |
| STATEN ISLAND | Tompkinsville-Stapleton-Clifton-Fox Hills | 42 | 19027 | 25.5 | 79.2 |
| STATEN ISLAND | Rosebank-Shore Acres-Park Hill | 34 | 25510 | 14.4 | 81.7 |
| STATEN ISLAND | West New Brighton-Silver Lake-Grymes Hill | 26 | 37010 | 9.9 | 91.2 |
| STATEN ISLAND | Port Richmond | 23 | 22609 | 22.0 | 83.2 |
| STATEN ISLAND | Grasmere-Arrochar-South Beach-Dongan Hills | 8 | 36259 | 11.3 | 85.4 |
| STATEN ISLAND | Annadale-Huguenot-Prince’s Bay-Woodrow | 6 | 40534 | 6.0 | 92.6 |
| STATEN ISLAND | New Dorp-Midland Beach | 5 | 29083 | 8.5 | 84.6 |
| STATEN ISLAND | Arden Heights-Rossville | 4 | 30683 | 4.3 | 92.3 |
Top 10 NTAs with the Highest Incident Rate in each boro
MANHATTAN
# Get the Top 10 NTAs with the Highest Incident Rate by Year
top_10_incident_rate_manhattan <- df_descriptive %>%
distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
filter(BORO == 'MANHATTAN') %>%
filter(NTAType == 'Residential') %>%
arrange(desc(incident_rate_by_year_nta)) %>%
slice(1:10)
# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_manhattan <- top_10_incident_rate_manhattan %>%
select(NTA, BORO, incident_rate = incident_rate_by_year_nta,
total_population = Total_population_nta,
percent_poverty = Percent_poverty,
percent_education = Percent_education)
# Display the table in the desired format
kable(top_10_nta_incident_rate_manhattan, digits = 6, caption = "Top 10 NTAs with the Highest Incident Rate by Year in MANHATTAN")| NTA | BORO | incident_rate | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| Inwood | MANHATTAN | 0.115706 | 36299 | 15.3 | 77.0 |
| East Harlem (North) | MANHATTAN | 0.086614 | 64655 | 32.3 | 75.9 |
| Harlem (North) | MANHATTAN | 0.080406 | 83327 | 25.1 | 82.9 |
| Inwood | MANHATTAN | 0.068872 | 36299 | 15.3 | 77.0 |
| Harlem (South) | MANHATTAN | 0.065799 | 47113 | 18.8 | 87.4 |
| Harlem (North) | MANHATTAN | 0.064805 | 83327 | 25.1 | 82.9 |
| East Harlem (North) | MANHATTAN | 0.061867 | 64655 | 32.3 | 75.9 |
| Harlem (South) | MANHATTAN | 0.057309 | 47113 | 18.8 | 87.4 |
| Harlem (North) | MANHATTAN | 0.055204 | 83327 | 25.1 | 82.9 |
| East Harlem (South) | MANHATTAN | 0.055171 | 59814 | 29.0 | 78.3 |
BROOKLYN
# Get the Top 10 NTAs with the Highest Incident Rate by Year
top_10_incident_rate_brooklyn <- df_descriptive %>%
distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
filter(BORO == 'BROOKLYN') %>%
filter(NTAType == 'Residential') %>%
arrange(desc(incident_rate_by_year_nta)) %>%
slice(1:10)
# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_brooklyn <- top_10_incident_rate_brooklyn %>%
select(NTA, BORO, incident_rate = incident_rate_by_year_nta,
total_population = Total_population_nta,
percent_poverty = Percent_poverty,
percent_education = Percent_education)
# Display the table in the desired format
kable(top_10_nta_incident_rate_brooklyn, digits = 6, caption = "Top 10 NTAs with the Highest Incident Rate by Year in BROOKLYN")| NTA | BORO | incident_rate | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| Brownsville | BROOKLYN | 0.153795 | 60470 | 37.2 | 76.9 |
| East New York-New Lots | BROOKLYN | 0.107539 | 53004 | 25.3 | 82.6 |
| Brownsville | BROOKLYN | 0.099223 | 60470 | 37.2 | 76.9 |
| Brownsville | BROOKLYN | 0.097569 | 60470 | 37.2 | 76.9 |
| Bedford-Stuyvesant (East) | BROOKLYN | 0.088597 | 84653 | 22.2 | 87.8 |
| East New York (North) | BROOKLYN | 0.079406 | 42818 | 25.5 | 79.4 |
| East New York-New Lots | BROOKLYN | 0.079239 | 53004 | 25.3 | 82.6 |
| East Flatbush-Remsen Village | BROOKLYN | 0.071584 | 39115 | 18.7 | 83.7 |
| Ocean Hill | BROOKLYN | 0.071142 | 37952 | 27.3 | 83.1 |
| Brownsville | BROOKLYN | 0.069456 | 60470 | 37.2 | 76.9 |
BRONX
# Get the Top 10 NTAs with the Highest Incident Rate by Year
top_10_incident_rate_bronx <- df_descriptive %>%
distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
filter(BORO == 'BRONX') %>%
filter(NTAType == 'Residential') %>%
arrange(desc(incident_rate_by_year_nta)) %>%
slice(1:10)
# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_bronx <- top_10_incident_rate_bronx %>%
select(NTA, BORO, incident_rate = incident_rate_by_year_nta,
total_population = Total_population_nta,
percent_poverty = Percent_poverty,
percent_education = Percent_education)
# Display the table in the desired format
kable(top_10_nta_incident_rate_bronx, digits = 6, caption = "Top 10 NTAs with the Highest Incident Rate by Year in BRONX")| NTA | BORO | incident_rate | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| Claremont Village-Claremont (East) | BRONX | 0.187350 | 24553 | 43.2 | 61.7 |
| Tremont | BRONX | 0.118196 | 32150 | 36.3 | 71.4 |
| Hunts Point | BRONX | 0.105743 | 15131 | 34.3 | 72.2 |
| Mount Eden-Claremont (West) | BRONX | 0.100703 | 49651 | 31.8 | 67.9 |
| Tremont | BRONX | 0.096423 | 32150 | 36.3 | 71.4 |
| West Farms | BRONX | 0.094307 | 20147 | 44.8 | 61.1 |
| Longwood | BRONX | 0.091836 | 40289 | 32.0 | 66.9 |
| Mount Hope | BRONX | 0.089615 | 49099 | 30.2 | 66.8 |
| West Farms | BRONX | 0.084380 | 20147 | 44.8 | 61.1 |
| Morrisania | BRONX | 0.082431 | 37607 | 35.7 | 65.9 |
QUEENS
# Get the Top 10 NTAs with the Highest Incident Rate by Year
top_10_incident_rate_queens <- df_descriptive %>%
distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
filter(BORO == 'QUEENS') %>%
filter(NTAType == 'Residential') %>%
arrange(desc(incident_rate_by_year_nta)) %>%
slice(1:10)
# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_queens <- top_10_incident_rate_queens %>%
select(NTA, BORO, incident_rate = incident_rate_by_year_nta,
total_population = Total_population_nta,
percent_poverty = Percent_poverty,
percent_education = Percent_education)
# Display the table in the desired format
kable(top_10_nta_incident_rate_queens, digits = 6, caption = "Top 10 NTAs with the Highest Incident Rate by Year in QUEENS")| NTA | BORO | incident_rate | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| South Jamaica | QUEENS | 0.069818 | 44401 | 13.4 | 78.9 |
| Rockaway Beach-Arverne-Edgemere | QUEENS | 0.067687 | 41367 | 22.1 | 82.1 |
| Far Rockaway-Bayswater | QUEENS | 0.064793 | 58648 | 18.3 | 79.9 |
| Queensbridge-Ravenswood-Dutch Kills | QUEENS | 0.054622 | 32954 | 16.5 | 85.0 |
| Laurelton | QUEENS | 0.053665 | 26088 | 5.4 | 91.2 |
| Rockaway Beach-Arverne-Edgemere | QUEENS | 0.050765 | 41367 | 22.1 | 82.1 |
| Richmond Hill | QUEENS | 0.046921 | 34100 | 10.6 | 79.8 |
| Queensbridge-Ravenswood-Dutch Kills | QUEENS | 0.045518 | 32954 | 16.5 | 85.0 |
| St. Albans | QUEENS | 0.044388 | 51816 | 8.6 | 88.8 |
| Richmond Hill | QUEENS | 0.043988 | 34100 | 10.6 | 79.8 |
STATEN ISLAND
# Get the Top 10 NTAs with the Highest Incident Rate by Year
top_10_incident_rate_staten <- df_descriptive %>%
distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
filter(BORO == 'STATEN ISLAND') %>%
filter(NTAType == 'Residential') %>%
arrange(desc(incident_rate_by_year_nta)) %>%
slice(1:10)
# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_staten <- top_10_incident_rate_staten %>%
select(NTA, BORO, incident_rate = incident_rate_by_year_nta,
total_population = Total_population_nta,
percent_poverty = Percent_poverty,
percent_education = Percent_education)
# Display the table in the desired format
kable(top_10_nta_incident_rate_staten, digits = 6, caption = "Top 10 NTAs with the Highest Incident Rate by Year in STATEN ISLAND")| NTA | BORO | incident_rate | total_population | percent_poverty | percent_education |
|---|---|---|---|---|---|
| St. George-New Brighton | STATEN ISLAND | 0.058397 | 20549 | 18.5 | 79.9 |
| Tompkinsville-Stapleton-Clifton-Fox Hills | STATEN ISLAND | 0.052557 | 19027 | 25.5 | 79.2 |
| Mariner’s Harbor-Arlington-Graniteville | STATEN ISLAND | 0.047773 | 33492 | 16.8 | 83.3 |
| Port Richmond | STATEN ISLAND | 0.044230 | 22609 | 22.0 | 83.2 |
| St. George-New Brighton | STATEN ISLAND | 0.043798 | 20549 | 18.5 | 79.9 |
| St. George-New Brighton | STATEN ISLAND | 0.038931 | 20549 | 18.5 | 79.9 |
| Mariner’s Harbor-Arlington-Graniteville | STATEN ISLAND | 0.038815 | 33492 | 16.8 | 83.3 |
| Tompkinsville-Stapleton-Clifton-Fox Hills | STATEN ISLAND | 0.036790 | 19027 | 25.5 | 79.2 |
| Rosebank-Shore Acres-Park Hill | STATEN ISLAND | 0.035280 | 25510 | 14.4 | 81.7 |
| St. George-New Brighton | STATEN ISLAND | 0.034065 | 20549 | 18.5 | 79.9 |
Number of Incidents counted by CDTAs in each Boroughs from 2017 to 2023
boroughs <- unique(cdta_map_data$BoroName)
for (b in boroughs) {
borough_data <- cdta_map_data %>%
filter(BoroName == b)
plot <- ggplot(data = borough_data) +
geom_sf(aes(fill = Number_of_Incidents), color = "black") +
geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") + # Add labels
scale_fill_gradientn(
colors = c( "green", "yellow", "red"), # Custom color scale
name = "Number of Incidents"
) +
labs(
title = paste("CDTA Incidents in", b),
subtitle = "2017 to 2023",
x = "Longitude",
y = "Latitude"
) +
theme_minimal()
print(plot)
}
